Electrocatalytic oxidation of hydrazine in alkaline media promoted by iron tetrapyridinoporphyrazine adsorbed on graphite surface

The electrocatalytic oxidation of hydrazine was studied using an ordinary pyrolytic graphite electrode modified with iron tetrapyridinoporphyrazine complex (FeTPyPz), employing cyclic voltammetry and rotating disk electrode techniques. Analyses of the voltammograms recorded at different potential scan rates and the polarization curves at different electrode rotation rates showed that the reaction of electrooxidation of hydrazine on FeTPyPz occurs via 4-electrons with the formation of N2 as main product. The kinetic parameters suggest that the second electron transfer step is rate controlling. The activity of FeTPyPz depends on its Fe(II)/Fe(I) formal potential and fits well in a volcano plot that includes several iron phthalocyanines, indicating that such formal potential is a good reactivity index for these complexes

Comet and cytogenetic tests as tools for evaluating genomic instability in seeds of Oryza sativa L. and Phaseolus vulgaris L. from gene banks

This study aimed to assess the feasibility of comet and cytogenetic tests as tools for evaluating genomic instability in seeds of Oryza sativa L. (rice) and Phaseolus vulgaris (beans) L. from gene banks. Rice and beans were exposed to methyl methanesulfonate (MMS) as a reference DNA damaging agent. Seeds of two accessions of rice and beans were obtained from Embrapa Rice and Beans - Brazil. Seed groups were imbibed in three concentrations of MMS for three periods of time to carry out cytogenetic tests, and for one period for the comet test. At concentrations of 10 and 15 mg/L, MMS induced cytotoxic and/or mutagenic effects in the meristematic cells of roots from all the accessions of both species. In the comet test, MMS induced genotoxic effects at all the concentrations in the evaluated accessions of rice and beans, except in one accession of beans at the lowest concentration (5 mg/L). Both species showed sensitivity to MMS. The comet test can be proposed for the measurement of genomic instability in accessions of rice and beans in gene banks, as being more sensitive than the cytogenetic tests used

Abundância de gavião-real e gavião-real falso numa área sob impacto de reservatório no Baixo e Médio rio Xingu

In the Brazilian Amazon, two monospecific genera, the Harpy Eagle and Crested Eagle have low densities and are classified by IUCN as Near Threatened due to habitat loss, deforestation, habitat degradation and hunting. In this study, we evaluate occurrence of these large raptors using the environmental surveys database from Belo Monte Hydroelectric Power Plant. Integrating the dataset from two methods, we plotted a distribution map along the Xingu River, including records over a 276-km stretch of river. Terrestrial surveys (RAPELD method) were more efficient for detecting large raptors than standardized aquatic surveys, although the latter were complementary in areas without modules. About 53% of the records were obtained during activities of wildlife rescue/flushing, vegetation suppression or in transit. Between 2012 and 2014, four Harpy Eagles were removed from the wild; two shooting victims, one injured by collision with power lines and one hit by a vehicle. Also, seven nests were mapped. The mean distance between Harpy Eagle records was 15 km along the river channel, with a mean of 20 km between nests near the channel, which allowed us to estimate 20 possible pairs using the alluvial forest, riverine forest and forest fragments. Territories of another ten pairs will probably be affected by inundation of the Volta Grande channel, which is far from the main river. The average distance between Crested Eagle records was 16 km along the river channel. The only nest found was 1.3 km away from a Harpy Eagle nest. The remnant forests are under threat of being replaced by cattle pastures, so we recommend that permanently protected riparian vegetation borders (APP) be guaranteed, and that forest fragments within 5 km of the river be conserved to maintain eagle populations. © 2015, Instituto Internacional de Ecologia. All rights reserved

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost